【佳學(xué)基因檢測(cè)】腫瘤純度采用新型配對(duì)特征選擇方法改善差異基因表達(dá)分析基因檢測(cè)

病原微生物基因檢測(cè)多少錢要點(diǎn)

體會(huì)癌的基因檢測(cè)基因解碼如何創(chuàng)新治療認(rèn)識(shí)到《Math Biosci》在?2019 May;311:39-48發(fā)表了一篇題目為《腫瘤純度采用新型配對(duì)特征選擇方法改善差異基因表達(dá)分析基因檢測(cè)》腫瘤靶向藥物治療基因檢測(cè)臨床研究文章。該研究由Sen Liang,?Sen Yang,?Dayang Liang,?Jiechao Ma,?Yuan Tian,?Jing Zhao,?Xu Zhang,?Ying Xu,?Yan Wang?等完成。促進(jìn)了腫瘤的正確治療與個(gè)性化用藥的發(fā)展，進(jìn)一步強(qiáng)調(diào)了基因信息檢測(cè)與分析的重要性。

腫瘤靶向藥物及正確治療臨床研究?jī)?nèi)容關(guān)鍵詞：

特征選擇,基因表達(dá)分析,匹配病例,對(duì)照設(shè)計(jì),檢驗(yàn)統(tǒng)計(jì),腫瘤純度

腫瘤靶向治療基因檢測(cè)臨床應(yīng)用結(jié)果

基于組織的基因表達(dá)數(shù)據(jù)分析雖然賊強(qiáng)大，但是與基于細(xì)胞的基因表達(dá)數(shù)據(jù)分析相比，它代表了一個(gè)更具挑戰(zhàn)性的問題，即使對(duì)于賊簡(jiǎn)單的差異基因表達(dá)分析也是如此。確定基因在腫瘤與非腫瘤對(duì)照組織中是否差異表達(dá)的結(jié)果不僅取決于兩個(gè)表達(dá)值，還取決于組織細(xì)胞為腫瘤細(xì)胞的百分比，即腫瘤純度。我們開發(fā)了一種新的匹配對(duì)特征選擇方法，該方法在確定基因是否在腫瘤與對(duì)照實(shí)驗(yàn)中差異表達(dá)時(shí)充分考慮了腫瘤純度，該方法簡(jiǎn)單、有效且正確。為了評(píng)估該方法的有效性和性能，我們將其與使用模擬數(shù)據(jù)集和實(shí)際癌癥組織數(shù)據(jù)集的四種已發(fā)表方法進(jìn)行了比較，發(fā)現(xiàn)我們的方法比其他方法具有更高的靈敏度和特異性，具有更好的性能。我們的方法是匹配病例對(duì)照設(shè)計(jì)下基因表達(dá)分析的配對(duì)特征選擇方法，該方法考慮了腫瘤純度信息，可為進(jìn)一步發(fā)展其他基因表達(dá)分析需求奠定基礎(chǔ)。

腫瘤發(fā)生與反復(fù)轉(zhuǎn)移國(guó)際數(shù)據(jù)庫(kù)描述：

Tissue-based gene expression data analyses, while most powerful, represent a significantly more challenging problem compared to cell-based gene expression data analyses, even for the simplest differential gene expression analyses. The result in determining if a gene is differentially expressed in tumor vs. non-tumorous control tissues does not only depend on the two expression values but also on the percentage of the tissue cells being tumor cells, i.e., the tumor purity. We developed a novel matched-pairs feature selection method, which takes into full consideration of the tumor purity when deciding if a gene is differentially expressed in tumor vs. control experiments, which is simple, effective, and accurate. To evaluate the validity and performance of the method, we have compared it with four published methods using both simulated datasets and actual cancer tissue datasets and found that our method achieved better performance with higher sensitivity and specificity than the other methods. Our method was the a matched-pairs feature selection method on gene expression analysis under matched case-control design which takes into consideration the tumor purity information, which can set a foundation for further development of other gene expression analysis needs.